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Article

Hybrid Symmetrical Uncertainty and Reference Set Harmony Search Algorithm for Gene Selection Problem

1
HLT Service Group Inc., 5818 S Archer Rd Suit 111, Summit, IL 60501, USA
2
Centre for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
*
Author to whom correspondence should be addressed.
Academic Editor: Christophe Guyeux
Mathematics 2022, 10(3), 374; https://doi.org/10.3390/math10030374
Received: 30 November 2021 / Revised: 30 December 2021 / Accepted: 5 January 2022 / Published: 26 January 2022
(This article belongs to the Special Issue Advances of Machine Learning and Their Applications)
Selecting the most miniature possible set of genes from microarray datasets for clinical diagnosis and prediction is one of the most challenging machine learning tasks. A robust gene selection technique is required to identify the most significant subset of genes by removing spurious or non-predictive genes from the original dataset without sacrificing or reducing classification accuracy. Numerous studies have attempted to address this issue by implementing either a filter or a wrapper. Although the filter approaches are computationally efficient, they are completely independent of the induction algorithm. On the other hand, wrapper approaches outperform filter approaches but are computationally more expensive. Therefore, this study proposes an enhanced gene selection method that uses a hybrid technique that combines the Symmetrical Uncertainty (SU) filter and Reference Set Harmony Search Algorithm (RSHSA) wrapper method, known as SU-RSHSA. The framework to develop the proposed SU-RSHSA includes numerous stages: (1) investigate a novel gene selection method based on the HSA and will then determine appropriate values for the HSA’s parameters, (2) enhance the construction process of the initial harmony memory while satisfying the diversity of the solution by embedding a reference set within the HSA (RSHSA), and (3) investigates the effect of integrating Symmetrical Uncertainty (SU) as a filter and RSHSA as a wrapper (SU-RSHSA) to maximize classification accuracy by leveraging their respective advantages. The results demonstrate that the SU-RSHSA outperforms the original HSA and SU-HSA in terms of classification accuracy, a small number of selected relevant genes, and reduced computational time. More importantly, the proposed SU-RSHSA gene selection method effectively generates a small subset of salient genes with high classification accuracy. View Full-Text
Keywords: symmetrical uncertainty; reference set; harmony search algorithm; gene selection symmetrical uncertainty; reference set; harmony search algorithm; gene selection
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MDPI and ACS Style

Shreem, S.S.; Ahmad Nazri, M.Z.; Abdullah, S.; Sani, N.S. Hybrid Symmetrical Uncertainty and Reference Set Harmony Search Algorithm for Gene Selection Problem. Mathematics 2022, 10, 374. https://doi.org/10.3390/math10030374

AMA Style

Shreem SS, Ahmad Nazri MZ, Abdullah S, Sani NS. Hybrid Symmetrical Uncertainty and Reference Set Harmony Search Algorithm for Gene Selection Problem. Mathematics. 2022; 10(3):374. https://doi.org/10.3390/math10030374

Chicago/Turabian Style

Shreem, Salam S., Mohd Z. Ahmad Nazri, Salwani Abdullah, and Nor S. Sani. 2022. "Hybrid Symmetrical Uncertainty and Reference Set Harmony Search Algorithm for Gene Selection Problem" Mathematics 10, no. 3: 374. https://doi.org/10.3390/math10030374

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